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Threadpool: take 2 (#8672)

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* Introduce ggml_compute_threadpool

- OpenMP functional: check
- Vanilla ggml functional: Check
- ggml w/threadpool functional: Check
- OpenMP no regression: No glaring problems
- Vanilla ggml no regression: No glaring problems
- ggml w/threadpool no regression: No glaring problems

* Minor fixes

* fixed use after release bug

* fixed a harmless race condition

* Fix Android bulid issue

* fix more race conditions

* fix deadlock for cases where cgraph.n_nodes == 1

and fix --poll case

* threadpool: use cpu_get_num_math to set the default number of threadpool threads

This way we avoid using E-Cores and Hyperthreaded siblings.

* bench: create fresh threadpool for each test

For benchmarking it's better to start a fresh pool for each test with the exact number of threads
needed for that test. Having larger pools is suboptimal (causes more load, etc).

* atomics: always use stdatomics with clang and use relaxed memory order when polling in ggml_barrier

This also removes sched_yield() calls from ggml_barrier() to match OpenMP behavior.

* threadpool: make polling the default to match openmp behavior

All command line args now allow for setting poll to 0 (false).

* threadpool: do not wakeup threads in already paused threadpool

* fix potential race condition in check_for_work

* threadpool: do not create two threadpools if their params are identical

* threadpool: reduce pause/resume/wakeup overhead in common cases

We now start threadpool in paused state only if we have two.
The resume is now implicit (ie new work) which allows for reduced locking and context-switch overhead.

* threadpool: add support for hybrid polling

poll params (--poll, ...) now specify "polling level", i.e. how aggresively we poll before waiting on cond.var.
poll=0 means no polling, 1 means poll for 128K rounds then wait, 2 for 256K rounds, ...

The default value of 50 (ie 50x128K rounds) seems like a decent default across modern platforms.
We can tune this further as things evolve.

* threadpool: reduce the number of barrier required

New work is now indicated with an atomic counter that is incremented for
each new graph that needs to be computed.
This removes the need for extra barrier for clearing the "new_work" and
removes the special case for trivial graphs.

* threadpool: remove special-casing for disposable threadpools

With the efficient hybrid polling there is no need to make disposable pools any different.
This simplifies the overall logic and reduces branching.

Include n_threads in debug print for disposable threadpool.

Declare pause and stop flags as atomic_bool
This doesn't actually generate any memory barriers and simply informs
the thread sanitizer that these flags can be written & read by different
threads without locking.

* threadpool: do not clear barrier counters between graphs computes (fixes race with small graphs)

This fixes the race condition with very small graphs where the main thread happens to
start a new graph while the workers are just about to exit from barriers.

* threadpool: use relaxed order for chunk sync

Full memory barrier is an overkill for this since each thread works on different chunk

* threadpool: remove abort_callback from threadpool state

* threadpool: better naming for thread/cpumask releated functions

* threadpool: consistent use of int type for n_threads params

* threadpool: add support for ggml_threadpool_params_default/init

Also removes the need for explicit mask_specified param.
all-zero cpumask means use default (usually inherited) cpu affinity mask.

* threadpool: move typedef into ggml.h

* threadpool: fix apply_priority() function name

* threadpool: fix swift wrapper errors due to n_threads int type cleanup

* threadpool: enable --cpu-mask and other threadpool related options only if threadpool is enabled

* threadpool: replace checks for compute_thread ret code with proper status check

* threadpool: simplify threadpool init logic and fix main thread affinity application

Most of the init code is now exactly the same between threadpool and openmp.

* threadpool: update threadpool resume/pause function names

* threadpool: enable openmp by default for now

* threadpool: don't forget to free workers state when omp is enabled

* threadpool: avoid updating process priority on the platforms that do not require it

On Windows we need to change overall process priority class in order to set thread priorities,
but on Linux, Mac, etc we do not need to touch the overall process settings.

* threadpool: update calling thread prio and affinity only at start/resume

This avoids extra syscalls for each graph_compute()

* llama-bench: turn threadpool params into vectors, add output headers, etc

* llama-bench: add support for cool off between tests --delay

This helps for long running tests on platforms that are thermally limited (phones, laptops, etc).
--delay (disabled by default) introduces the sleep for N seconds before starting each test.

* threadpool: move process priority setting into the apps (bench and cli)

This avoids changing the overall process priority on Windows for the apps
that use ggml/llama.cpp directy.

* threadpool: move all pause/resume logic into ggml

* threadpool: futher api cleanup and prep for future refactoring

All threadpool related functions and structs use ggml_threadpool prefix.

* threadpool: minor indent fixes

* threadpool: improve setprioty error message

* Update examples/llama-bench/llama-bench.cpp

Co-authored-by: slaren <slarengh@gmail.com>

* threadpool: fix indent in set_threadpool call

* use int32_t for n_thread type in public llama.cpp API

* threadpool: use _new and _free instead of _create and _release

* fix two more public APIs to use int32_t for n_threads

* build: set _GNU_SOURCE for Adroid

---------

Co-authored-by: Max Krasnyansky <quic_maxk@quicinc.com>
Co-authored-by: fmz <quic_fzaghlou@quic.com>
Co-authored-by: Max Krasnyansky <max.krasnyansky@gmail.com>
Co-authored-by: slaren <slarengh@gmail.com>
This commit is contained in:
Faisal Zaghloul 2024-08-29 19:20:53 -04:00 • committed by GitHub
parent 9f7d4bcf5c
commit 42c76d1358
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GPG key ID: B5690EEEBB952194
22 changed files with 1310 additions and 257 deletions
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350
common/common.cpp
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@ -251,6 +251,57 @@ int32_t cpu_get_num_math() {
return cpu_get_num_physical_cores();
}
// Helper for setting process priority
#if defined(_WIN32)
bool set_process_priority(enum ggml_sched_priority prio) {
if (prio == GGML_SCHED_PRIO_NORMAL) {
return true;
}
DWORD p = NORMAL_PRIORITY_CLASS;
switch (prio) {
case GGML_SCHED_PRIO_NORMAL: p = NORMAL_PRIORITY_CLASS; break;
case GGML_SCHED_PRIO_MEDIUM: p = ABOVE_NORMAL_PRIORITY_CLASS; break;
case GGML_SCHED_PRIO_HIGH: p = HIGH_PRIORITY_CLASS; break;
case GGML_SCHED_PRIO_REALTIME: p = REALTIME_PRIORITY_CLASS; break;
}
if (!SetPriorityClass(GetCurrentProcess(), p)) {
fprintf(stderr, "warn: failed to set process priority class %d : (%d)\n", prio, (int) GetLastError());
return false;
}
return true;
}
#else // MacOS and POSIX
#include <sys/types.h>
#include <sys/resource.h>
bool set_process_priority(enum ggml_sched_priority prio) {
if (prio == GGML_SCHED_PRIO_NORMAL) {
return true;
}
int p = 0;
switch (prio) {
case GGML_SCHED_PRIO_NORMAL: p = 0; break;
case GGML_SCHED_PRIO_MEDIUM: p = -5; break;
case GGML_SCHED_PRIO_HIGH: p = -10; break;
case GGML_SCHED_PRIO_REALTIME: p = -20; break;
}
if (!setpriority(PRIO_PROCESS, 0, p)) {
fprintf(stderr, "warn: failed to set process priority %d : %s (%d)\n", prio, strerror(errno), errno);
return false;
}
return true;
}
#endif
//
// CLI argument parsing
//
@ -277,6 +328,30 @@ void gpt_params_handle_model_default(gpt_params & params) {
}
}
void postprocess_cpu_params(cpu_params& cpuparams, const cpu_params* role_model) {
int32_t n_set = 0;
if (cpuparams.n_threads < 0) {
// Assuming everything about cpuparams is invalid
if (role_model != nullptr) {
cpuparams = *role_model;
} else {
cpuparams.n_threads = cpu_get_num_math();
}
}
for (int32_t i = 0; i < GGML_MAX_N_THREADS; i++) {
if (cpuparams.cpumask[i]) {
n_set++;
}
}
if (n_set && n_set < cpuparams.n_threads) {
// Not enough set bits, may experience performance issues.
fprintf(stderr, "warn: Not enough set bits in CPU mask (%d) to satisfy requested thread count: %d\n", n_set, cpuparams.n_threads);
}
}
bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
bool invalid_param = false;
std::string arg;
@ -296,6 +371,11 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
}
}
postprocess_cpu_params(params.cpuparams, nullptr);
postprocess_cpu_params(params.cpuparams_batch, &params.cpuparams);
postprocess_cpu_params(params.draft_cpuparams, &params.cpuparams);
postprocess_cpu_params(params.draft_cpuparams_batch, &params.cpuparams_batch);
if (params.prompt_cache_all && (params.interactive || params.interactive_first)) {
throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
}
@ -331,7 +411,7 @@ void gpt_params_parse_from_env(gpt_params & params) {
get_env("LLAMA_ARG_MODEL_ALIAS", params.model_alias);
get_env("LLAMA_ARG_HF_REPO", params.hf_repo);
get_env("LLAMA_ARG_HF_FILE", params.hf_file);
get_env("LLAMA_ARG_THREADS", params.n_threads);
get_env("LLAMA_ARG_THREADS", params.cpuparams.n_threads);
get_env("LLAMA_ARG_CTX_SIZE", params.n_ctx);
get_env("LLAMA_ARG_N_PARALLEL", params.n_parallel);
get_env("LLAMA_ARG_BATCH", params.n_batch);
@ -368,6 +448,79 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
return true;
}
bool parse_cpu_range(const std::string & range, bool (&boolmask)[GGML_MAX_N_THREADS]) {
size_t dash_loc = range.find('-');
if (dash_loc == std::string::npos) {
fprintf(stderr, "Format of CPU range is invalid! Expected [<start>]-[<end>].\n");
return false;
}
size_t start_i;
size_t end_i;
if (dash_loc == 0) {
start_i = 0;
} else {
start_i = std::stoull(range.substr(0, dash_loc));
if (start_i >= GGML_MAX_N_THREADS) {
fprintf(stderr, "Start index out of bounds!\n");
return false;
}
}
if (dash_loc == range.length() - 1) {
end_i = GGML_MAX_N_THREADS - 1;
} else {
end_i = std::stoull(range.substr(dash_loc + 1));
if (end_i >= GGML_MAX_N_THREADS) {
fprintf(stderr, "End index out of bounds!\n");
return false;
}
}
for (size_t i = start_i; i <= end_i; i++) {
boolmask[i] = true;
}
return true;
}
bool parse_cpu_mask(const std::string & mask, bool (&boolmask)[GGML_MAX_N_THREADS]) {
// Discard potential 0x prefix
size_t start_i = 0;
if (mask.length() >= 2 && mask.substr(0, 2) == "0x") {
start_i = 2;
}
size_t num_digits = mask.length() - start_i;
if (num_digits > 128) num_digits = 128;
size_t end_i = num_digits + start_i;
for (size_t i = start_i, n = (num_digits*4 - 1); i < end_i; i++, n-=4) {
char c = mask.at(i);
int8_t id = c;
if ((c >= '0' && c <= '9')) {
id -= '0';
} else if (c >= 'a' && c <= 'f') {
id -= 'a' - 10;
} else if (c >= 'A' && c <= 'F') {
id -= 'A' - 10;
} else {
fprintf(stderr, "Invalid hex character '%c' at position %d\n", c, int32_t(i));
return false;
}
boolmask[ n ] = boolmask[ n ] || ((id & 8) != 0);
boolmask[n - 1] = boolmask[n - 1] || ((id & 4) != 0);
boolmask[n - 2] = boolmask[n - 2] || ((id & 2) != 0);
boolmask[n - 3] = boolmask[n - 3] || ((id & 1) != 0);
}
return true;
}
#define CHECK_ARG if (++i >= argc) { invalid_param = true; return true; }
bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param) {
@ -384,36 +537,142 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
}
if (arg == "-t" || arg == "--threads") {
CHECK_ARG
params.n_threads = std::stoi(argv[i]);
if (params.n_threads <= 0) {
params.n_threads = std::thread::hardware_concurrency();
params.cpuparams.n_threads = std::stoi(argv[i]);
if (params.cpuparams.n_threads <= 0) {
params.cpuparams.n_threads = std::thread::hardware_concurrency();
}
return true;
}
if (arg == "-C" || arg == "--cpu-mask") {
CHECK_ARG
std::string mask = argv[i];
params.cpuparams.mask_valid = true;
invalid_param = !parse_cpu_mask(mask, params.cpuparams.cpumask);
return true;
}
if (arg == "-Cr" || arg == "--cpu-range") {
CHECK_ARG
std::string range = argv[i];
params.cpuparams.mask_valid = true;
invalid_param = !parse_cpu_range(range, params.cpuparams.cpumask);
return true;
}
if (arg == "--prio") {
CHECK_ARG
params.cpuparams.priority = (enum ggml_sched_priority) std::stoul(argv[i]);
return true;
}
if (arg == "--cpu-strict") {
CHECK_ARG
params.cpuparams.strict_cpu = std::stoul(argv[i]);
return true;
}
if (arg == "--poll") {
CHECK_ARG
params.cpuparams.poll = std::stoul(argv[i]);
return true;
}
if (arg == "-tb" || arg == "--threads-batch") {
CHECK_ARG
params.n_threads_batch = std::stoi(argv[i]);
if (params.n_threads_batch <= 0) {
params.n_threads_batch = std::thread::hardware_concurrency();
params.cpuparams_batch.n_threads = std::stoi(argv[i]);
if (params.cpuparams_batch.n_threads <= 0) {
params.cpuparams_batch.n_threads = std::thread::hardware_concurrency();
}
return true;
}
if (arg == "-Cb" || arg == "--cpu-mask-batch") {
CHECK_ARG
std::string mask = argv[i];
params.cpuparams_batch.mask_valid = true;
invalid_param = !parse_cpu_mask(mask, params.cpuparams_batch.cpumask);
return true;
}
if (arg == "-Crb" || arg == "--cpu-range_batch") {
CHECK_ARG
std::string range = argv[i];
params.cpuparams_batch.mask_valid = true;
invalid_param = !parse_cpu_range(range, params.cpuparams_batch.cpumask);
return true;
}
if (arg == "--prio-batch") {
CHECK_ARG
params.cpuparams_batch.priority = (enum ggml_sched_priority) std::stoul(argv[i]);
return true;
}
if (arg == "--cpu-strict-batch") {
params.cpuparams_batch.strict_cpu = true;
return true;
}
if (arg == "--poll-batch") {
CHECK_ARG
params.cpuparams_batch.poll = std::stoul(argv[i]);
return true;
}
if (arg == "-td" || arg == "--threads-draft") {
CHECK_ARG
params.n_threads_draft = std::stoi(argv[i]);
if (params.n_threads_draft <= 0) {
params.n_threads_draft = std::thread::hardware_concurrency();
params.draft_cpuparams.n_threads = std::stoi(argv[i]);
if (params.draft_cpuparams.n_threads <= 0) {
params.draft_cpuparams.n_threads = std::thread::hardware_concurrency();
}
return true;
}
if (arg == "-Cd" || arg == "--cpu-mask-draft") {
CHECK_ARG
std::string mask = argv[i];
params.draft_cpuparams.mask_valid = true;
invalid_param = !parse_cpu_mask(mask, params.draft_cpuparams.cpumask);
return true;
}
if (arg == "-Crd" || arg == "--cpu-range-draft") {
CHECK_ARG
std::string range = argv[i];
params.draft_cpuparams.mask_valid = true;
invalid_param = !parse_cpu_range(range, params.draft_cpuparams.cpumask);
return true;
}
if (arg == "--prio-draft") {
CHECK_ARG
params.draft_cpuparams.priority = (enum ggml_sched_priority) std::stoul(argv[i]);
return true;
}
if (arg == "--cpu-strict-draft") {
params.draft_cpuparams.strict_cpu = true;
return true;
}
if (arg == "--poll-draft") {
CHECK_ARG
params.draft_cpuparams.poll = std::stoul(argv[i]);
return true;
}
if (arg == "-tbd" || arg == "--threads-batch-draft") {
CHECK_ARG
params.n_threads_batch_draft = std::stoi(argv[i]);
if (params.n_threads_batch_draft <= 0) {
params.n_threads_batch_draft = std::thread::hardware_concurrency();
params.draft_cpuparams_batch.n_threads = std::stoi(argv[i]);
if (params.draft_cpuparams_batch.n_threads <= 0) {
params.draft_cpuparams_batch.n_threads = std::thread::hardware_concurrency();
}
return true;
}
if (arg == "-Crbd" || arg == "--cpu-range-batch-draft") {
CHECK_ARG
std::string range = argv[i];
params.draft_cpuparams_batch.mask_valid = true;
invalid_param = !parse_cpu_range(range, params.draft_cpuparams_batch.cpumask);
return true;
}
if (arg == "--prio-batch-draft") {
CHECK_ARG
params.draft_cpuparams_batch.priority = (enum ggml_sched_priority) std::stoul(argv[i]);
return true;
}
if (arg == "--cpu-strict-batch-draft") {
params.draft_cpuparams_batch.strict_cpu = true;
return true;
}
if (arg == "--poll-batch-draft") {
CHECK_ARG
params.draft_cpuparams_batch.poll = std::stoul(argv[i]);
return true;
}
if (arg == "-p" || arg == "--prompt") {
CHECK_ARG
params.prompt = argv[i];
@ -1498,11 +1757,40 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
options.push_back({ "*", " --no-display-prompt", "don't print prompt at generation (default: %s)", !params.display_prompt ? "true" : "false" });
options.push_back({ "*", "-co, --color", "colorise output to distinguish prompt and user input from generations (default: %s)", params.use_color ? "true" : "false" });
options.push_back({ "*", "-s, --seed SEED", "RNG seed (default: %d, use random seed for < 0)", params.seed });
options.push_back({ "*", "-t, --threads N", "number of threads to use during generation (default: %d)", params.n_threads });
options.push_back({ "*", "-t, --threads N", "number of threads to use during generation (default: %d)", params.cpuparams.n_threads });
options.push_back({ "*", "-tb, --threads-batch N", "number of threads to use during batch and prompt processing (default: same as --threads)" });
options.push_back({ "speculative", "-td, --threads-draft N", "number of threads to use during generation (default: same as --threads)" });
options.push_back({ "speculative", "-tbd, --threads-batch-draft N",
"number of threads to use during batch and prompt processing (default: same as --threads-draft)" });
options.push_back({ "speculative", "-tbd, --threads-batch-draft N","number of threads to use during batch and prompt processing (default: same as --threads-draft)" });
#ifndef GGML_USE_OPENMP
// these options are available only with the internal threadpool
options.push_back({ "*", "-C, --cpu-mask M", "CPU affinity mask: arbitrarily long hex. Complements cpu-range (default: \"\")"});
options.push_back({ "*", "-Cr, --cpu-range lo-hi", "range of CPUs for affinity. Complements --cpu-mask"});
options.push_back({ "*", " --cpu-strict <0|1>", "use strict CPU placement (default: %u)\n", (unsigned) params.cpuparams.strict_cpu});
options.push_back({ "*", " --priority N", "set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.cpuparams.priority});
options.push_back({ "*", " --poll <0...100>", "use polling level to wait for work (0 - no polling, default: %u)\n", (unsigned) params.cpuparams.poll});
options.push_back({ "*", "-Cb, --cpu-mask-batch M", "CPU affinity mask: arbitrarily long hex. Complements cpu-range-batch (default: same as --cpu-mask)"});
options.push_back({ "*", "-Crb, --cpu-range-batch lo-hi", "ranges of CPUs for affinity. Complements --cpu-mask-batch"});
options.push_back({ "*", " --cpu-strict-batch <0|1>","use strict CPU placement (default: same as --cpu-strict)"});
options.push_back({ "*", " --priority-batch N", "set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: --priority)"});
options.push_back({ "*", " --poll-batch <0|1>", "use polling to wait for work (default: same as --poll"});
options.push_back({ "speculative", "-Cd, --cpu-mask-draft M", "Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask)"});
options.push_back({ "speculative", "-Crd, --cpu-range-draft lo-hi", "Ranges of CPUs for affinity. Complements --cpu-mask-draft"});
options.push_back({ "speculative", " --cpu-strict-draft <0|1>","Use strict CPU placement for draft model (default: same as --cpu-strict)"});
options.push_back({ "speculative", " --priority-draft N", "Set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: same as --priority)"});
options.push_back({ "speculative", " --poll-draft <0|1>", "Use polling to wait for draft model work (default: same as --poll])"});
options.push_back({ "speculative", "-Cbd, --cpu-mask-batch-draft M","Draft model CPU affinity mask. Complements cpu-range-draft-batch (default: same as --cpu-mask-draft)"});
options.push_back({ "speculative", "-Crbd, --cpu-range-batch-draft lo-hi",
"Ranges of CPUs for affinity. Complements --cpu-mask-draft-batch)"});
options.push_back({ "speculative", " --cpu-strict-batch-draft <0|1>",
"Use strict CPU placement for draft model (default: --cpu-strict-draft)"});
options.push_back({ "speculative", " --priority-batch-draft N","Set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: --priority-draft)"});
options.push_back({ "speculative", " --poll-batch-draft <0|1>","Use polling to wait for draft model work (default: --poll-draft)"});
#endif // GGML_USE_OPENMP
options.push_back({ "speculative", " --draft N", "number of tokens to draft for speculative decoding (default: %d)", params.n_draft });
options.push_back({ "speculative", "-ps, --p-split N", "speculative decoding split probability (default: %.1f)", (double)params.p_split });
options.push_back({ "*", "-lcs, --lookup-cache-static FNAME",
@ -1774,7 +2062,6 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
options.push_back({ "export-lora", "-m, --model", "model path from which to load base model (default '%s')", params.model.c_str() });
options.push_back({ "export-lora", " --lora FNAME", "path to LoRA adapter (can be repeated to use multiple adapters)" });
options.push_back({ "export-lora", " --lora-scaled FNAME S", "path to LoRA adapter with user defined scaling S (can be repeated to use multiple adapters)" });
options.push_back({ "*", "-t, --threads N", "number of threads to use during computation (default: %d)", params.n_threads });
options.push_back({ "export-lora", "-o, --output FNAME", "output file (default: '%s')", params.lora_outfile.c_str() });
printf("usage: %s [options]\n", argv[0]);
@ -1806,9 +2093,9 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
std::string gpt_params_get_system_info(const gpt_params & params) {
std::ostringstream os;
os << "system_info: n_threads = " << params.n_threads;
if (params.n_threads_batch != -1) {
os << " (n_threads_batch = " << params.n_threads_batch << ")";
os << "system_info: n_threads = " << params.cpuparams.n_threads;
if (params.cpuparams_batch.n_threads != -1) {
os << " (n_threads_batch = " << params.cpuparams_batch.n_threads << ")";
}
#if defined(_WIN32) && (_WIN32_WINNT >= 0x0601) && !defined(__MINGW64__) // windows 7 and later
// TODO: windows + arm64 + mingw64
@ -2332,8 +2619,9 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
cparams.n_seq_max = params.n_parallel;
cparams.n_batch = params.n_batch;
cparams.n_ubatch = params.n_ubatch;
cparams.n_threads = params.n_threads;
cparams.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
cparams.n_threads = params.cpuparams.n_threads;
cparams.n_threads_batch = params.cpuparams_batch.n_threads == -1 ?
params.cpuparams.n_threads : params.cpuparams_batch.n_threads;
cparams.seed = params.seed;
cparams.logits_all = params.logits_all;
cparams.embeddings = params.embedding;
@ -2359,6 +2647,22 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
return cparams;
}
struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_params & params) {
struct ggml_threadpool_params tpp;
ggml_threadpool_params_init(&tpp, params.n_threads); // setup the defaults
if (params.mask_valid) {
std::memcpy(&tpp.cpumask, &params.cpumask, GGML_MAX_N_THREADS);
}
tpp.prio = params.priority;
tpp.poll = params.poll;
tpp.strict_cpu = params.strict_cpu;
return tpp;
}
#ifdef LLAMA_USE_CURL
static bool starts_with(const std::string & str, const std::string & prefix) {
@ -3348,7 +3652,7 @@ void yaml_dump_non_result_info(FILE * stream, const gpt_params & params, const l
yaml_dump_vector_float(stream, "tensor_split", tensor_split_vector);
fprintf(stream, "tfs: %f # default: 1.0\n", sparams.tfs_z);
fprintf(stream, "threads: %d # default: %u\n", params.n_threads, std::thread::hardware_concurrency());
fprintf(stream, "threads: %d # default: %u\n", params.cpuparams.n_threads, std::thread::hardware_concurrency());
fprintf(stream, "top_k: %d # default: 40\n", sparams.top_k);
fprintf(stream, "top_p: %f # default: 0.95\n", sparams.top_p);
fprintf(stream, "min_p: %f # default: 0.0\n", sparams.min_p);